Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach
Yunfan Gerry Zhang, Vishal Gajjar, Griffin Foster, Andrew Siemion,, James Cordes, Casey Law, and Yu Wang

TL;DR
This paper presents a machine learning method for detecting and analyzing pulses from the repeating fast radio burst FRB 121102, achieving higher sensitivity and speed, and introduces a new periodicity search technique that constrains possible emission periods.
Contribution
The study develops a neural network-based detection method outperforming traditional algorithms and introduces a novel periodicity analysis technique for FRB data.
Findings
Detected 72 new pulses from FRB 121102, totaling 93 pulses in five hours.
Ruled out constant periodicities >10 ms in the burst arrival times with 99% confidence.
Demonstrated the effectiveness of machine learning in high-sensitivity, low-false-positive pulse detection.
Abstract
We report the detection of 72 new pulses from the repeating fast radio burst FRB 121102 in Breakthrough Listen C-band (4-8 GHz) observations at the Green Bank Telescope. The new pulses were found with a convolutional neural network in data taken on August 26, 2017, where 21 bursts have been previously detected. Our technique combines neural network detection with dedispersion verification. For the current application we demonstrate its advantage over a traditional brute-force dedis- persion algorithm in terms of higher sensitivity, lower false positive rates, and faster computational speed. Together with the 21 previously reported pulses, this observa- tion marks the highest number of FRB 121102 pulses from a single observation, total- ing 93 pulses in five hours, including 45 pulses within the first 30 minutes. The number of data points reveal trends in pulse fluence, pulse detection…
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